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An Epistemic Dynamic Model for Tagging Systems
 

An Epistemic Dynamic Model for Tagging Systems

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This presentation is about our paper which was presented at the Hypertext conference 2008. ...

This presentation is about our paper which was presented at the Hypertext conference 2008.

Abstract: In recent literature, several models were proposed for reproducing and understanding the tagging behavior of users. They all assume that the tagging behavior is influenced by the previous tag assignments of other users. But they are only partially successful in reproducing characteristic properties found in tag streams. We argue that this inadequacy of existing models results from their inability to include user’s background knowledge into their model of tagging behavior. This paper presents a generative tagging model that integrates both components, the background knowledge and the influence of previous tag assignments. Our model successfully reproduces characteristic properties of
tag streams. It even explains effects of the user interface on the tag stream.

Links to the paper:
http://dx.doi.org/10.1145/1379092.1379109
http://www.uni-koblenz.de/~staab/Research/Publications/2008/DellschaftStaabHypertext08.pdf

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An Epistemic Dynamic Model for Tagging Systems An Epistemic Dynamic Model for Tagging Systems Presentation Transcript

  • <is web> Information Systems & Semantic Web University of Koblenz ▪ Landau, Germany An Epistemic Dynamic Model for Tagging Systems Klaas Dellschaft and Steffen Staab {klaasd, staab}@uni-koblenz.de
  • Delicious Tagging Interface <is web> How do users influence each other in tagging systems? Hypothesis: Tagging involves imitation of other users AND selection of tags from background knowledge of users. ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 2 of 24
  • Understanding Dynamics in Tagging Systems <is web> User interface Something else? Tagging Conceptualization Behavior Comparison Shared of Statistics Own Background Knowledge terminology Model of User Interface Influence Simulated Joint Stochastic Model Tagging Behavior Model of Own Model of Sharing Background KnowledgeISWeb - Information Systems Klaas Dellschaft Hypertext 2008& Semantic Web klaasd@uni-koblenz.de 3 of 24
  • Folksonomies <is web> Vertexes: Users, tags, resources Hyperedges: Tag assignments (user X tag X resource) Postings:  Tag assignments of a user to a single resource  Can be ordered according to their time-stamp ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 4 of 24
  • Co-occurrence Streams <is web> Co-occurrence Streams:  All tags co-occurring with a given tag in a posting  Ordered by posting time Example tag assignments for ‘ajax:  {mackz, r1, {ajax, javascript}, 13:25}  {klaasd, r2, {ajax, rss, web2.0}, 13:26}  {mackz, r2, {ajax, php, javascript}, 13:27} Resulting co-occurrence stream: javascript rss web2.0 php javascript time Tag |Y| |U| |T| |R| ajax 2.949.614 88.526 41.898 71.525 blog 6.098.471 158.578 186.043 557.017 xml 974.866 44.326 31.998 61.843 ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 5 of 24
  • Co-occurrence Streams – Tag Growth <is web> Linear scaling of axes Logarithmic scaling of axes ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 6 of 24
  • Co-occurrence Streams – Tag Frequencies <is web> Logarithmic scaling of axes ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 7 of 24
  • Resource Streams <is web> Resource Streams:  All tags assigned to a resource  Ordered by posting time Example tag assignments:  {mackz, r1, {ajax, javascript}, 13:25}  {klaasd, r2, {ajax, rss, web2.0}, 13:26}  {mackz, r2, {ajax, php, javascript}, 13:27} Resulting resource stream: ajax rss web2.0 ajax php javascript time ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 8 of 24
  • Resource Streams – Tag Frequencies <is web> Logarithmic scaling of axes ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 9 of 24
  • Resource Streams – Tag Frequencies <is web> Logarithmic scaling of axes Taken from Halpin, Robu and Shepard, WWW 2007 ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 10 of 24
  • Existing Models <is web> Frequency Rank Co-occur. Streams Resource Streams Tag Growth Polya Urn Model O O Fixed size Simon Model O O Linear YS Model w/ Memory + O Linear Halpin et al. Model O O Linear Observed Properties Long tail w/ cut-off Drop around tag 7 Sublinear ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 11 of 24
  • Stochastic Models of Influence <is web> User interface Something else? Tagging Conceptualization Behavior Comparison Shared of Statistics Own Background Knowledge terminology Model of User Interface Influence Simulated Joint Stochastic Model Tagging Behavior Model of Own Model of Sharing Background Knowledge ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 12 of 24
  • Modeling Tag Imitation <is web> Delicious Tagging Interface  Imitation of previous tag assignments  Free input of new tags (taken from background knowledge of a user) ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 13 of 24
  • Simulating a Tag Stream <is web> Start with empty tag stream Each simulation step appends a new tag assignment: p(w|t): Probability of selecting word w for topic t. Modeled by word distributions in a topic centered text corpus. K PB 1- P BK n: Number of visible previous tags. h: Maximal number of previous tag assignments used for determining ranking of the n distinct tags. ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 14 of 24
  • Modeling Background Knowledge <is web> Text Corpora Del.icio.us Text Corpora PBK: Probability of selecting from background knowledge  p(w|t): Probability of selecting word w for topic t. Modeled by word distributions in a topic centered text corpus.  p(w|r): Probability of selecting word w for resource r. ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 15 of 24
  • Modeling Tag Imitation <is web> PBK t t-1 t-2 t-3 t-4 t-5 … t-h … 1-PBK 1 2 3 … n PI = 1 - PBK: Probability of imitating a previous tag assignment  n: Number of visible top-ranked tags  h: Maximal number of previous tag assignments used for determining ranking of the n distinct tags ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 16 of 24
  • Simulation Results <is web> User interface Something else? Tagging Conceptualization Behavior Comparison Shared of Statistics Own Background Knowledge terminology Model of User Interface Influence Simulated Joint Stochastic Model Tagging Behavior Model of Own Model of Sharing Background Knowledge ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 17 of 24
  • Simulating Co-occurrence Streams <is web> Tag growth:  Influenced by PBK and p(w|t) Tag Frequencies:  Influenced by PBK, p(w|t), n, h  n: Semantic breadth of a topic (blog: 100 tags, ajax: 50 tags, xml: 50 tags; Cattuto et al. 2007)  h: No hint for realistic values. Good guess may be a value around 1000. ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 18 of 24
  • Co-occ. Streams – Simulated Tag Growth <is web>PI: Imitation PI PBKPBK: Background Knowl. Logarithmic scaling of axes ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 19 of 24
  • Co-occ. Streams – Simulated Tag Frequencies <is web>PI: Imitation PI PBKPBK: Background Knowl. PI PBK PI PBKn: Visible Tagsh: Available History Logarithmic scaling of axes ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 20 of 24
  • Simulating Ajax, Blog and XML <is web>PI: Imitation (PI (PIPBK: Background Knowl. (PIn: Visible Tagsh: Available History blog ajaxObservation xmlSimulation Logarithmic scaling of axes ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 21 of 24
  • Res. Streams – Simulated Tag Frequencies <is web>PI: Imitation PI PBK PI PBKPBK: Background Knowl.n: Visible Tagsh: Available History Logarithmic scaling of axes ISWeb - Information Systems Klaas Dellschaft Hypertext 2008 & Semantic Web klaasd@uni-koblenz.de 22 of 24
  • Conclusions <is web>• Imitation AND background knowledge are needed for explaining properties of tag streams• Probability of imitating previous tag assignments: ~60-90% Frequency Rank Co-occur. Streams Resource Streams Tag Growth Polya Urn Model O O Fixed size Simon Model O O Linear YS Model w/ Memory + O Linear Halpin et al. Model O O Linear Epistemic Model + + Sublinear Observed Properties Long tail w/ cut-off Drop around tag 7 SublinearISWeb - Information Systems Klaas Dellschaft Hypertext 2008& Semantic Web klaasd@uni-koblenz.de 23 of 24
  • <is web> Data sets and simulation software: http://isweb.uni-koblenz.de/Research/Tagdataset http://www.tagora-project.euISWeb - Information Systems Klaas Dellschaft Hypertext 2008& Semantic Web klaasd@uni-koblenz.de 24 of 24